Sampling and Data Analysis in Market Research

Posted on May 30, 2024 in Mathematics

PHASES OF THE SAMPLING PROCESS

  1. Define the population.
  2. Identify criteria for sample selection.
  3. Determine sample size.
  4. Choose a sample selection procedure.
  5. Select sample units.

FACTORS THAT CONDITION THE CALCULATION OF THE SAMPLE SIZE

  1. Parameter homogeneity or heterogeneity: More heterogeneous results require a larger sample.
  2. Acceptable margin of error: Determines precision of estimate.
  3. The level of confidence with which you want to work.
  4. Available resources: both economic and time availability.
  5. Sampling method chosen.
  6. Data analysis technique.

TYPES OF SAMPLING

Sampling in statistics aims to obtain a representative sample from a larger population.

1. Probabilistic or Random Sampling

Each population element has a known and non-zero chance of selection, ensuring equal opportunity for all members.

2. Non-Probabilistic or Empirical Sampling

Inclusion probabilities are not precisely known or calculable. Selection is often based on researcher convenience or criteria rather than equal probability for all elements.

PROBABILISTIC OR RANDOM SAMPLING METHODS

  1. Simple random sampling: Each population element has an equal chance of selection, chosen entirely at random.
  2. Systematic random sampling: Elements are chosen at regular intervals after selecting an initial random element.
  3. Stratified sampling: Population is divided into groups based on relevant characteristics, with random samples taken from each group.
  4. Cluster sampling: Population is divided into clusters, and random clusters are chosen, with all elements within chosen clusters included.
  5. Area sampling: Population is divided into geographic areas, with random areas selected, and all elements within chosen areas included.
  6. Random route sampling: Random routes within a defined area are chosen, and all elements along these routes are included in the sample.

NON-PROBABILISTIC OR EMPIRICAL SAMPLE METHODS

  1. Convenience sampling: Selecting accessible or convenient population elements without specific random criteria.
  2. Sample by judgment or criterion: Elements chosen based on researcher opinion or expertise.
  3. Quota sampling: Selecting elements to reflect specific population characteristics or proportions.
  4. Itinerary sampling: Choosing population elements along defined routes or itineraries.
  5. Oversampling: Selecting more elements from certain groups to ensure representation.
  6. Rational sampling: Selecting elements based on specific relevant characteristics, without random selection.
  7. Snowball sampling: Starting with a small group and using their referrals to select additional participants, creating a “snowball” effect.

SAMPLING ERROR AND NON-SAMPLING ERROR

When working with a sample instead of the entire population, errors must be considered. Two types of errors exist:

  1. Sampling error: Measures the precision of sample results compared to the population. It’s the researcher’s decision on the acceptable level of error based on study objectives, statistical criteria, and available resources. It’s essentially the cost of analyzing a sample instead of the entire population.
  2. Non-sampling error: Precision not related to sample design but fieldwork execution and other factors. Key types include:
    1. Coverage errors: Arise when the complete population list is unavailable, leading to duplications, omissions, or outdated records.
    2. Non-response errors: Result from units’ unwillingness or inability to participate, introducing bias and potentially affecting sample representativeness.

Reduction of Sampling Error

The sampling error, or margin of error, is the maximum expected difference between sample and population values. It indicates the range where the true population value is likely to fall. Typically, errors range from ±2% to ±3.5%, and larger samples help reduce this error, but feasibility constraints often limit this option.

ANALYSIS AND EVALUATION OF THE OBTAINED DATA

Market research can be conducted through:

  1. Desk research: Using existing published sources (secondary information) for speed and low cost.
  2. Fieldwork: Collecting data via face-to-face or remote surveys/interviews (primary information).
  3. Mixed method: Combining desk research with fieldwork to obtain necessary and high-quality data.

Process includes:

  1. Data collection.
  2. Data preparation.
  3. Data analysis.
  4. Drawing conclusions.

DATA ORGANIZATION AND TABULATION

Four phases of data preparation are:

1. Codification

  1. Coding and editing prepare questionnaires for accurate transcription. Coding assigns digits to questionnaire data, with post-fieldwork coding for open-ended questions.

2. Creation of a Data Folder

  1. Creation of a data file: Transfer data to a computer file for statistical analysis. Record questionnaire answers in a suitable format, often compatible with data analysis software.

3. Consistency Analysis

Verify data accuracy and coherence for research reliability. To ensure data consistency, three control phases are essential:

  1. Control of filters and quotas:
    1. Check responses to filter questions to confirm interviewees align with target audience.
    2. Verify adherence to quotas if target audience includes specific proportions (e.g., 90% male workers in a sector).
  2. Variable verification:
    1. Confirm absence of labeling errors or data entry mistakes in variables.
    2. Check sequence of answers for accuracy.
  3. Verification of relationship between variables:
    1. Ensure logical relationships between variables are accurate.
    2. Compare obtained information with existing data for consistency.

4. Tabulation

Tabulation involves compiling data from questionnaires into well-organized tables. These tables, typically created using spreadsheets, databases, or statistical software, provide a general overview of the collected information in a format suitable for analysis.

STATISTICAL DATA ANALYSIS

Before collecting data, it’s crucial to plan for the analysis, determining what data will be collected and in what format. Statistical analysis can be categorized into:

  • Univariate analysis: Focuses on a single variable, revealing its characteristics.
  • Bivariate analysis: Examines the relationship between two variables, typically a dependent and an independent variable.
  • Multivariate analysis: Explores relationships between multiple variables simultaneously, offering a comprehensive analysis beyond bivariate analysis.

DESCRIPTIVE STATISTICAL MEASURES

Descriptive statistical techniques provide an initial analysis, representation, and description of data. Descriptive measures include:

  • Measures of central tendency.
  • Measures of non-central tendency.
  • Measures of dispersion.
  • Measures of distribution shape.

Dispersion Measures

Variance: Average of squared deviations from the mean, indicating data dispersion.

  • High variance: Data more dispersed.
  • Low variance: Values closer to mean.
  • Variance of zero: All values equal, coinciding with mean.

Standard deviation: Square root of mean squared deviations from mean, measuring spread of data.

  • High standard deviation: Data more dispersed.
  • Low standard deviation: Values closer to mean.